Welcome to this statistics coded notebook. This notebook developed with Rmd will allow to reproduce the graphics presented in the Statistics Explained article on Enforcement of Immigration Legislation statistics.

This article presents indicators on the enforcement of immigration legislation. It provides statistics on: third country or non-European Union (EU) citizens who were refused entry at the external borders of the EU-27 ¹; non-EU citizens who were illegally present on the territory of an EU Member State; and non-EU citizens who were ordered to leave the territory of an EU Member State ². Each of these indicators can be regarded as an official record of persons subject to the enforcement of EU immigration legislation, providing a general overview of the outcomes of territorial surveillance and control procedures.

ref_tot <- setDT(get_eurostat("migr_eirfs"))
app_tot<- setDT(get_eurostat("migr_eipre"))
ord_tot <- setDT(get_eurostat("migr_eiord"))
ret_tot <- setDT(get_eurostat("migr_eirtn"))
ret_type <- setDT(get_eurostat("migr_eirt_vol"))
ret_ass <- setDT(get_eurostat("migr_eirt_ass"))

map <- get_eurostat_geospatial(
  output_class = "sf",
  resolution = "60",
  nuts_level = "0",
  year = "2016"
)

Non-EU citizens subject to the enforcement of immigration legislation in EU Member States, 2009-2019

refused <- ref_tot[geo == "EU28" & reason == "TOTAL" & indic_mg == "TOT_REF" & !(citizen %in% c("TOTAL","EUR_OTH","AME","AFR","ASI","EUR","OCE")), sum(values, na.rm = T),time]

apprehended <- app_tot[geo == "EU28" & !(citizen %in% c("TOTAL","EUR_C_E_OTH","AME","AFR","ASI","EUR","OCE")) & age == "TOTAL" & sex == "T", sum(values, na.rm = T),time]

ordered <- ord_tot[geo == "EU28" & sex == "T" & !(citizen %in% c("TOTAL","EUR_C_E_OTH","AME","AFR","ASI","EUR","OCE")) & age == "TOTAL", sum(values, na.rm =T ),time]

returned <- ret_tot[geo == "EU28" & sex == "T" & !(citizen %in% c("TOTAL","EUR_C_E_OTH","EUR_OTH","AME","AFR","ASI","EUR","OCE")) & age == "TOTAL" & indic_mg == "TOT_RET", sum(values, na.rm =T ),time]

dt <- copy(refused)
dt[,apprehended := apprehended[,V1]]
dt[,ordered := ordered[1:8,V1]]
dt[,returned := returned[1:8,V1]]
setnames(dt,"V1","refused")

plot_ly(dt, x = ~time,y= ~refused,
               name = "Refused",type = "scatter",mode = 'lines+markers') %>%
  add_trace(y = ~apprehended, name = 'Apprehended', mode = 'lines+markers') %>%
  add_trace(y = ~ordered, name = 'Ordered to leave', mode = 'lines+markers') %>%
  add_trace(y = ~returned, name = 'Returned', mode = 'lines+markers') %>%
  layout(paper_bgcolor='gray20',
           plot_bgcolor='gray87',
           xaxis = list(color = 'black',tickangle = 0),
           yaxis = list(color = 'black', title = "Persons")
    ) %>%
  layout(hovermode = "x unified")

Non-EU citizens subject to the enforcement of immigration legislation in EU Member States EU28, 2009-2019 (number)

Non-EU citizens found to be illegally present

Overview

Map

tmap_mode("view")
app_map <- app_tot[time == "2020-01-01" & !(citizen %in% c("TOTAL","EUR_C_E_OTH","AME","AFR","ASI","EUR","OCE")) & age == "TOTAL" & sex == "T", sum(values, na.rm = T),geo]
setnames(app_map,"V1","Apprehended")

map <- left_join(map, app_map, by="geo")%>%
  tm_shape() +
  tm_fill("Apprehended",
    popup.vars = c("Apprehended","NUTS_ID", "NUTS_NAME"),
    palette = "Oranges", 
    breaks = c(0,1000, 5000, 25000, 50000, Inf),
    title = "Persons found to be illegally present"
  ) +
  tm_borders() +
  tm_layout(bg.color = "lightblue")

map

Chart

app <- app_tot[!(geo %in% c("EU27_2020","EU28")) & !(citizen %in% c("TOTAL","EUR_C_E_OTH","AME","AFR","ASI","EUR","OCE")) & age == "TOTAL" & sex == "T",]
app[!(geo %in% c("DE","EL","ES","FR","HU")), geo := "OTH_MS"]
app <- app[,sum(values,na.rm = T), .(geo, time)]
app <- dcast(app, time~geo, value.var = "V1")

plot_ly(app, x = ~time,y= ~DE,
               name = "Germany",type = "scatter",mode = 'lines+markers') %>%
  add_trace(y = ~EL, name = 'Greece', mode = 'lines+markers') %>%
  add_trace(y = ~ES, name = 'Spain', mode = 'lines+markers') %>%
  add_trace(y = ~FR, name = 'France', mode = 'lines+markers') %>%
  add_trace(y = ~HU, name = 'Hungary', mode = 'lines+markers') %>%
  add_trace(y = ~OTH_MS, name = 'Other Member States', mode = 'lines+markers') %>%
  layout(paper_bgcolor='gray20',
           plot_bgcolor='gray87',
           xaxis = list(color = 'black',tickangle = 0),
           yaxis = list(color = 'black', title = "Persons")
    ) %>%
  layout(hovermode = "x unified")

test

Non-EU citizens found to be illegally present — by sex and age

Bar chart

app_bar <- app_tot[geo == "EU28" & !(citizen %in% c("TOTAL","EUR_C_E_OTH","AME","AFR","ASI","EUR","OCE")) & !(age %in% c("TOTAL","UNK","Y_LT18")) & sex != "T" & time == "2019-01-01", sum(values, na.rm = T),.(sex,age)] %>% 
  dcast( age~sex, value.var = "V1")

app_bar[age == "Y18-34", age := '18-34 years']
app_bar[age == "Y14-17", age := '14-17 years']
app_bar[age == "Y_LT14", age := '< 14 years']
app_bar[age == "Y_GE35", age := '>= 35 years']
app_bar <- app_bar[c(3,2,1,4)]


tit3 <- "Non-EU citizens found to be illegally present in the EU-27, by sex and age - 2019"
plot_ly(app_bar, x = ~F, y = ~age, type = 'bar', orientation = 'h', name = 'Women',
        marker = list(color = 'gold',
                      line = list(color = 'gold',
                                  width = 3)))%>%
  add_trace(x = ~M, name = 'Men',
            marker = list(color = 'darkblue',
                          line = list(color = 'darkblue',
                                      width = 3)))%>%
  layout(barmode = 'stack',
         xaxis = list(title = "Number of persons"),
         yaxis = list(title ="Age"),
         title=tit3)

Non-EU citizens found to be illegally present in the EU-27, by sex and age - 2019

Table

Top 20 countries of citizenship of non-EU citizens found to be illegally present in the EU-27, 2014-2019

app_tab <- app_tot[!(citizen %in% c("TOTAL","EUR_C_E_OTH","AME","AFR","ASI","EUR","OCE")) & age == "TOTAL" & sex == "T" & time <= "2020-01-01" & time >= "2015-01-01",sum(values,na.rm = T),.(citizen,time) ]

top20 <- app_tab[,sum(V1,na.rm=T),citizen]
setnames(top20, "V1","Total")
top20 <- top20[order(-Total)]
top20 <- top20[1:20,]

app_tab[,time:=substring(time,1,4)]
app_tab <-  dcast(app_tab, citizen~time, value.var = "V1")

app_tab <- left_join(top20, app_tab)

datatable(app_tab[1:20], rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )

Non-EU citizens ordered to leave the EU-27

Table

Non-EU citizens ordered to leave the territory of an EU Member State, the United Kingdom or an EFTA country, 2015-2020

ord_tab <-  ord_tot[sex == "T" & citizen == "TOTAL" & age == "TOTAL"& time >= "2015-01-01" ,sum(values,na.rm=T),.(geo,time)]
ord_tab[,time:=substring(time,1,4)]
ord_tab <-  dcast(ord_tab, geo~time, value.var = "V1")
datatable(ord_tab, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )

Bar chart

Top 20 countries of citizenship of non-EU citizens ordered to leave the EU-27, 2020 and 2019

ord_tab <-  ord_tot[geo == "EU28" & sex == "T" & age == "TOTAL"& !(citizen %in% c("TOTAL","EUR_C_E_OTH","AME","AFR","ASI","EUR","OCE")) & time <= "2019-01-01" & time >= "2018-01-01",sum(values,na.rm = T),.(citizen,time)]
ord_tab[,time:=substring(time,1,4)]
top20 <- ord_tab[time == "2019",]
top20 <- top20[order(-V1),]
top20 <- top20[1:20, .(citizen)]

ord_tab <- left_join(top20,ord_tab)
ord_tab <-  dcast(ord_tab, citizen~time, value.var = "V1")
setnames(ord_tab, names(ord_tab), c("citizen","year_1","year"))

plot_ly(ord_tab, x = ~citizen, y = ~year_1, type = 'bar', name = '2019')%>% 
  add_trace(y = ~year, name = '2020')%>%
  layout(barmode = 'group',
         xaxis = list(title = "Citizenship"),
         yaxis = list(title ="Number of persons"),
         hovermode='x unified')

Top 20 countries of citizenship of non-EU citizens ordered to leave the EU-27, 2020 and 2019

Returns of non-EU citizens

Overview

ret_tab <-  ret_tot[geo == "EU28" & sex == "T" & !(citizen %in% c("TOTAL","EUR_C_E_OTH","EUR_OTH","AME","AFR","ASI","EUR","OCE")) & age == "TOTAL" & indic_mg == "RET_THRD" & (time =="2019-01-01" | time == "2018-01-01"), sum(values, na.rm =T ),.(citizen,time)]
ret_tab[,time:=substring(time,1,4)]
top20 <- ret_tab[time == "2019",]
top20 <- top20[order(-V1),]
top20 <- top20[1:20, .(citizen)]

ret_tab <- left_join(top20,ret_tab)
ret_tab <-  dcast(ret_tab, citizen~time, value.var = "V1")
setnames(ret_tab, names(ret_tab), c("citizen","year_1","year"))

plot_ly(ret_tab, x = ~citizen, y = ~year_1, type = 'bar', name = '2018')%>% 
  add_trace(y = ~year, name = '2019')%>%
  layout(barmode = 'group',
         xaxis = list(title = "Citizenship"),
         yaxis = list(title ="Number of persons"),
         hovermode='x unified')

Top 20 countries of citizenship of non-EU citizens returned outside the EU-27, 2018 and 2019

Types of returns and assistance received

ret_tab2 <-  ret_type[!(citizen %in% c("TOTAL","EUR_C_E_OTH","EUR_C_E_OTH","AME","AFR","ASI","EUR","OCE")) & (time =="2020-01-01"), sum(values, na.rm =T ),.(geo,indic_mg)]

ret_tab2 <-  dcast(ret_tab2, geo~indic_mg, value.var = "V1")
ret_tab2 <- ret_tab2[,lapply(.SD, function(x) round(x/TOT_RET*100)), .SDcols=c("ENFORCE","OTH","VOLUNT"), by = geo]

plot_ly(ret_tab2, x = ~geo, y = ~ENFORCE, type = 'bar', name = 'Enforced return')%>% 
    add_trace(y = ~OTH, name = 'Other')%>%
  add_trace(y = ~VOLUNT, name = 'Voluntary return')%>%
    layout(barmode = 'stack',
           xaxis = list(title = ""),
           yaxis = list(title ="%"),
           hovermode='x unified')

Non-EU citizens returned outside the EU-27 or EFTA countries, by type of return (2020)

ret_tab3 <-  ret_ass[!(citizen %in% c("TOTAL","EUR_C_E_OTH","EUR_C_E_OTH","AME","AFR","ASI","EUR","OCE")) & (time =="2019-01-01"), sum(values, na.rm =T ),.(geo,indic_mg)]

ret_tab3 <-  dcast(ret_tab3, geo~indic_mg, value.var = "V1")
ret_tab3 <- ret_tab3[,lapply(.SD, function(x) round(x/TOT_RET*100)), .SDcols=c("ASSIST","N_ASSIST","UNK"), by = geo]

plot_ly(ret_tab3, x = ~geo, y = ~ASSIST, type = 'bar', name = 'Assisted return')%>% 
    add_trace(y = ~N_ASSIST, name = 'Non assisted return')%>%
  add_trace(y = ~UNK, name = 'Unkown')%>%
    layout(barmode = 'stack',
           xaxis = list(title = ""),
           yaxis = list(title ="%"),
           hovermode='x unified')

Non-EU citizens who left the EU-27 or EFTA countries, by type of assistance received, 2020

Non-EU citizens refused entry into the EU-27

Overview

ref_pie <- ref_tot[geo !="EU27_2020" & time == "2020-01-01" & reason == "TOTAL" & indic_mg == "TOT_REF" ,.(geo,values) ]
plot_ly( data = ref_pie, labels = ~geo, values = ~values, type = "pie", textinfo = "label+percent", insidetextorientation = "radial" )

Share of non-EU citizens refused entry into the EU-27, 2020

Bar charts

ref_tab <-  ref_tot[!(geo %in% c("EU28","EU27_2020")) &reason == "TOTAL" & !(citizen %in% c("TOTAL","EUR_OTH","AME","AFR","ASI","EUR","OCE")) & time == "2019-01-01", sum(values, na.rm = T), .(citizen, indic_mg)]

top20 <- ref_tab[indic_mg=="TOT_REF"]
top20 <- top20[order(-V1)]
top20 <- top20[1:20,.(citizen)]

ref_tab <- left_join(top20, ref_tab)
ref_tab <-  dcast(ref_tab, citizen~indic_mg, value.var = "V1")

plot_ly(ref_tab, x = ~citizen, y = ~REF_AIR, type = 'bar', name = 'Air')%>% 
    add_trace(y = ~REF_LAND, name = 'Land')%>%
  add_trace(y = ~REF_SEA, name = 'Sea')%>%
    layout(barmode = 'stack',
           xaxis = list(title = ""),
           yaxis = list(title ="%"),
           hovermode='x unified')

Top 20 countries of citizenship of non-EU citizens refused entry into the EU-27, by type of border, 2019

ref_tab2 <-  ref_tot[!(geo %in% c("EU28","EU27_2020")) &reason != "TOTAL" &indic_mg == "TOT_REF" & !(citizen %in% c("TOTAL","EUR_OTH","AME","AFR","ASI","EUR","OCE")) & time == "2019-01-01", sum(values, na.rm = T), .(reason)]

plot_ly(ref_tab2, x = ~reason, y = ~V1, type = 'bar', name = 'Reason')%>% 
    layout(barmode = 'stack',
           xaxis = list(title = ""),
           yaxis = list(title ="Number"),
           hovermode='x unified')

Non-EU citizens refused entry into the EU-27, by grounds of entry refusal, 2019

EU aggregates are computed as the sum of the national statistics available for the EU Member States. It is possible that the statistics for the EU involve some double counting of individuals if they are found to be illegally present in more than one Member State.↩︎
Statistics on the enforcement of immigration legislation refer to the concept of external borders for all EU Member States and EFTA countries, even if some of these are not in the Schengen area. The external borders of the Schengen area do not coincide with the external borders of the EU Member States due to: opt-outs for Ireland from the Schengen area; Bulgaria, Croatia, Cyprus and Romania are not yet members of the Schengen area; Iceland, Liechtenstein, Norway and Switzerland are part of the Schengen area but are not members of the EU.↩︎

A work by Jules Zaccardi

Enforcement of Immigration Legislation

Eurostat - Asylum and Managed Migration team

27 May 2021

Non-EU citizens subject to the enforcement of immigration legislation in EU Member States, 2009-2019

Non-EU citizens found to be illegally present

Overview

Map

Chart

Non-EU citizens found to be illegally present — by sex and age

Bar chart

Table

Non-EU citizens ordered to leave the EU-27

Table

Bar chart

Returns of non-EU citizens

Overview

Types of returns and assistance received

Non-EU citizens refused entry into the EU-27

Overview

Bar charts